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Dept     : ECE
Sem      : 5
Regul    : 2019
Faculty : Dr.M.Sudha
phone  : 8903444955
E-mail  : sudha.m.ece@snsce.ac.in
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Announcements

  • Youtube Video

    Dear Students the Youtube Video has been uploaded for the following topics:</br>Unsupervised learning</br>Hierarchical clustering</br>Neurons and Neural networks introduction

  • Question Bank

    Dear Students the Question Bank has been uploaded for the following topics:</br>UNSUPERVISED LEARNING, </br>NEURONS & NEURAL NETWORKS, </br>DEEP LEARNING

  • Assignment

    Assignment topic is Case study -Application - speech recognition and due date is 30-10-2024.

  • Assignment

    Assignment topic is Case study -Application - speech recognition and due date is 29-10-2024.

  • Assignment

    Assignment topic is Case study -Application - speech recognition and due date is 18-10-2024.

  • Resource Link

    Dear Students the Resource Link has been uploaded for the following topics:</br>Recurrent Neural Networks</br>Supervised and Unsupervised learning

  • Puzzles

    Dear Students the Puzzles has been uploaded for the following topics:</br>Linear Regression and Multivariate Regression, </br>Hierarchical Clustering , </br>Multilayer Perceptron- Back Propagation -Dimensionality Reduction, </br>Convolutional Networks, Recurrent Neural Networks, Bidirectional RNNs

  • Announcement*

    Dear Students the Announcement* has been uploaded for the following topics:</br>Definition of learning systems-, </br>Goals and applications of machine learning, </br>Types of Machine Learning, </br>Machine Learning Process-Terminology, </br>Weight Space-The Curse of Dimensionality, </br>Testing Machine Learning Algorithms.

  • Resource Link

    Dear Students the Resource Link has been uploaded for the following topics:</br>Introduction to Artificial Intelligence</br>Introduction to Artificial Neural Networks</br>Deep Learning

  • Lecture Notes

    Dear Students the Lecture Notes has been uploaded for the following topics:</br>Regression: Linear Regression –Parametric Models, </br>Multivariate Regression., </br>Classification: Bayesian Decision Theory, </br>parametric and non-parametric methods, </br>Logistic Regression- K-Nearest Neighbor classifier, </br>Decision Tree based methods for classification and Regression

  • Lecture Notes

    Dear Students the Lecture Notes has been uploaded for the following topics:</br>The Brain and The Neuron, </br>Neural Networks, </br>Perceptron-Training the perceptron, </br>Perceptron Learning Algorithm, </br>Multilayer Perceptron- Back Propagation , </br>Dimensionality Reduction., </br>The Brain and The Neuron, </br>Convolutional Networks, </br>Recurrent Neural Networks, </br>Bidirectional RNNs, </br>Deep Recurrent Networks, </br>Recursive Neural Networks, </br>Applications – Speech Recognition

  • Question Bank

    Dear Students the Question Bank has been uploaded for the following topics:</br>FUNDAMENTALS OF MACHINE LEARNING, </br>FUNDAMENTALS OF MACHINE LEARNING, </br>SUPERVISED LEARNING

  • Assignment

    Assignment topic is Machine learning process and testing and due date is 10-09-2024.

  • Announcement

    Dear Students, Go through the puzzles and solve it. Hint also gave . Further if any clarification exits plz discuss.

  • Announcement

    Dear Students, Assignment posted under the topic of Machine Learning Process and testing . Kindly submit as on date.

  • Announcement

    Dear Students, Assignment posted under the topic of Machine Learning Process and testing . Kindly submit as on date.

  • Youtube Video

    Dear Students the Youtube Video has been uploaded for the following topics:</br>Introduction to AI and ML</br> How AI works, Definition of Learning systems

  • Puzzles

    Dear Students the Puzzles has been uploaded for the following topics:</br>Classification

  • Assignment

    Assignment topic is AI and ML and due date is .

  • Lecture Notes

    Dear Students the Lecture Notes has been uploaded for the following topics:</br>Introduction-Clustering, </br>Introduction-Clustering, </br>K-means clustering, </br>EM algorithm,, </br>Hierarchical Clustering, </br>Principal Component Analysis, </br>Definition of learning systems-, </br>Goals and applications of machine learning, </br>Types of Machine Learning, </br>Machine Learning Process-Terminology, </br>Weight Space-The Curse of Dimensionality, </br>Testing Machine Learning Algorithms.